Foundations and Trends® in Communications and Information Theory > Concentration of Measure Inequalities in Information Theory, Communications, and Coding

© 2013 M. Raginsky and I. Sason

Coding theory and practice, Information theory and statistics, Multiuser information theory, Shannon theory

260 pp. $99.00

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260 pp. $230.00

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1. Introduction

2. Concentration Inequalities via the Martingale Approach

3. The Entropy Method, Logarithmic Sobolev Inequalities, and Transportation-Cost Inequalities

Acknowledgments

References

Concentration inequalities have been the subject of exciting developments during the last two decades, and have been intensively studied and used as a powerful tool in various areas. These include convex geometry, functional analysis, statistical physics, mathematical statistics, pure and applied probability theory (e.g., concentration of measure phenomena in random graphs, random matrices, and percolation), information theory, theoretical computer science, learning theory, and dynamical systems.

*Concentration of Measure Inequalities in Information Theory, Communications, and Coding* focuses on some of the key modern mathematical tools that are used for the derivation of concentration inequalities, on their links to information theory, and on their various applications to communications and coding. In addition to being a survey, this monograph also includes various new recent results derived by the authors.

*Concentration of Measure Inequalities in Information Theory, Communications, and Coding* is essential reading for all researchers and scientists in information theory and coding.

This is the second edition of Concentration of Measure Inequalities in Information Theory, Communications, and Coding**ISBN: **978-1-60198-906-2